The Impact of Biomedical Innovation on Longevity and Health

Frank R. Lichtenberg

Columbia University andNational Bureau of Economic Research

August 31, 2012

The Impact of Biomedical Innovation on Longevity and Health

Frank R. Lichtenberg Columbia University and

National Bureau of Economic Research

31 August 2012

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uring the 20th century, U.S. life expectancy at birth increased by almost 30 years (63%),

from 47.3 years in 1900 to 77.0 years in 2000. Nordhaus (2002) estimated that, “to a

first approximation, the economic value of increases in longevity over the twentieth century is

about as large as the value of measured growth in non-health goods and services” (p. 17).

Murphy and Topel (2005) observed that “the historical gains from increased longevity have

been enormous. Over the 20th century, cumulative gains in life expectancy were worth over

$1.2 million per person for both men and women. Between 1970 and 2000 increased longevity

added about $3.2 trillion per year to national wealth, an uncounted value equal to about half

of average annual GDP over the period.” There has also been a significant decline over time in

the functional limitations of older people in the United States, and probably worldwide.

A number of authors have expressed the view that a substantial portion of recent gains in the

longevity and health of Americans is due to biomedical research and innovation. Cutler, Deaton

and Lleras-Muney (2006) “tentatively identif[ied] the application of scientific advance and

technical progress (some of which is induced by income and facilitated by education) as the

ultimate determinant of health.” Kramarow et al (2007) suggested that “medical advances

have had an important role in the better health of older Americans.” Fuchs (2010) stated that

“since World War II … biomedical innovations (new drugs, devices, and procedures) have been

the primary source of increases in longevity.” And the National Institutes of Health (2012) says

that “thanks in large part to NIH research, Americans are living nearly 30 years longer than they

did in 1900.”

I have conducted a number of studies that have sought to measure the impact of biomedical

innovation on the longevity and health of Americans and other populations during recent

decades. I have examined the effects of several different types of medical innovation. Most of

my studies have examined the impact of pharmaceutical innovation, which is easier to measure

than other types of medical innovation (e.g., surgical procedure innovation). Moreover,

pharmaceuticals are more “research intensive” than other types of medical care: In 2007,

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prescription drugs accounted for just 10% of national health expenditure,1 but Dorsey et al.

(2010) estimated that more than half of U.S. funding for biomedical research came from

pharmaceutical and biotechnology firms. Moreover, Sampat and Lichtenberg (2011)

demonstrated that new drugs often build on upstream government research. I have also

examined the impact of diagnostic imaging innovation (CT scans and MRIs) in several studies

and the impact of inpatient hospital procedure innovation in one study (Lichtenberg (2011d)).

There are several ways to measure medical innovation. I believe that the best measure is the

(mean) vintage of the medical goods and services used by an individual or population. One

definition of vintage is “a period of origin or manufacture (e.g., a piano of 1845 vintage)”

(http://www.merriam-webster.com/dictionary/vintage). Solow (1960) introduced the concept

of vintage into economic analysis; this was one of the contributions to the theory of economic

growth that the Royal Swedish Academy of Sciences (1987) cited when it awarded Solow the

1987 Alfred Nobel Memorial Prize in Economic Sciences. Solow’s basic idea was that technical

progress is “built into” machines and other goods and that this must be taken into account

when making empirical measurements of their roles in production. I have often used a drug’s

initial FDA approval date as a measure of its vintage. Sometimes it is not feasible to measure

the vintage of medical goods and services. In these cases, other measures of innovation, such

as the number of distinct products (e.g., drugs) available for treating a condition, can be used.

I have attempted to assess the impact of medical innovation on a variety of health outcomes.

The outcome that I have studied the most is longevity -- the “quantity of life.” Longevity is the

best-measured health outcome; it may also be the most important one. I have also studied the

impact of medical (i.e., pharmaceutical) innovation on functional status, or “quality of life” --

the ability of people to engage in important activities. In the nonelderly adult population, the

activity of greatest interest is working. In the elderly population, the activities of greatest

interest are “activities of daily living” such as dressing, eating, and bathing. In addition, I have

analyzed the impact of medical innovation on the probability of being admitted to a hospital or

nursing home, events that are indicative of poor health. 1 http://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-

Reports/NationalHealthExpendData/Downloads/tables.pdf, Table 2.

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I have used several approaches (research designs) to measure the impact of biomedical

innovation on longevity and functional status. Each approach has advantages and

disadvantages. Some of my studies have been based on cross-sectional patient-level data.

Others have been based on longitudinal, region-level data; they have investigated whether

regions (e.g., states) undergoing more rapid medical innovation have exhibited larger

improvements in health. And some of my studies have been based on longitudinal, disease-

level data; they have investigated whether the medical conditions undergoing more rapid

innovation have exhibited larger gains in health outcomes.

In this article, I will briefly summarize five of my econometric studies of the impact of medical

innovation on longevity and health. The first study examined the effect of pharmaceutical

innovation, diagnostic imaging innovation, and other factors on longevity and medical

expenditure growth using longitudinal state-level data during the period 1991-2004. The

second study investigated the impact of the introduction of new orphan drugs on premature

mortality from rare diseases using longitudinal, disease-level data from two countries: the U.S.

(during the period 1999-2006) and France (during the period 2000-2007). The third study

examined the effect of pharmaceutical innovation on the fraction of the working-age

population receiving Social Security Disability Insurance benefits, again using longitudinal state-

level data. The fourth study examined the effect of pharmaceutical innovation on the

functional status of nursing home residents using cross-sectional, patient-level data from the

2004 National Nursing Home Survey. The fifth study examined the effect of cardiovascular

system drug innovation on hospitalization and mortality due to cardiovascular disease using

longitudinal country-level data on 20 OECD countries during the period 1995-2003.

The Quality of Medical Care, Behavioral Risk Factors, and Longevity Growth

The rate of longevity increase has varied considerably across U.S. states since 1991. In the eight

states with the smallest increase, life expectancy at birth increased by only 0.31-1.16 years

between 1991 and 2004. In the eight states with the largest increase, life expectancy increased

by 2.5-4.3 years. Lichtenberg (2011a) examined the effect of the quality of medical care,

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behavioral risk factors, and other variables on life expectancy and medical expenditure using

longitudinal state-level data. That study examined the effects of three measures of the quality

of medical care. The first is the average quality of diagnostic imaging procedures, defined as

the fraction of procedures that are advanced procedures. The second is the average quality of

practicing physicians, defined as the fraction of physicians that were trained at top-ranked

medical schools. The third is the mean vintage (FDA approval year) of outpatient and inpatient

prescription drugs.

I also examined the effects on longevity of three important behavioral risk factors —obesity,

smoking, and AIDS incidence — and other variables — education, income, and health insurance

coverage — that might be expected to influence longevity growth. My econometric approach

controlled for the effects of unobserved factors that vary across states but are relatively stable

over time (e.g., climate and environmental quality) and unobserved factors that change over

time but are invariant across states (e.g., changes in federal government policies).

My indicators of the quality of diagnostic imaging procedures, drugs, and physicians almost

always had positive and statistically significant effects on life expectancy. Life expectancy

increased more rapidly in states where (1) the fraction of Medicare diagnostic imaging

procedures that were advanced procedures increased more rapidly; (2) the vintage of self- and

provider-administered drugs increased more rapidly; and (3) the quality of medical schools

previously attended by physicians increased more rapidly.

Between 1991 and 2004, life expectancy at birth increased 2.37 years. The estimates implied

that, during this period, the increased use of advanced imaging technology increased life

expectancy by 0.62-0.71 years, use of newer outpatient prescription drugs increased life

expectancy by 0.96-1.26 years, and use of newer provider-administered drugs increased life

expectancy by 0.48-0.54 years. The decline in the average quality of medical schools previously

attended by physicians reduced life expectancy by 0.28-0.47 years.

The rise from 44% to 59% in the fraction of the population that was overweight or obese

reduced the increase in life expectancy by .58-.68 years. The decline in the incidence of AIDS is

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estimated to have increased life expectancy by .18-.20 years. The small decline in smoking

prevalence may have increased life expectancy by about 0.10 years.

Growth in life expectancy was uncorrelated across states with health insurance coverage and

education, and inversely correlated with per-capita income growth. The 19% increase in real

per-capita income is estimated to have reduced life expectancy by .34-.43 years. The sum of

the contributions of all of the factors to the increase in life expectancy is in the 0.85-1.32 year

range. Consequently, between 1.05 and 1.52 years of the 2.37-year increase in life expectancy

is unexplained.

I performed several tests of the robustness of the life expectancy model. Controlling for per-

capita health expenditure (the “quantity” of healthcare) and eliminating the influence of infant

mortality had virtually no effect on the healthcare quality coefficients. Controlling for the

adoption of an important nonmedical innovation also had little influence on the estimated

effects of medical innovation adoption on life expectancy.

Although states with larger increases in the quality of diagnostic procedures, drugs, and

physicians had larger increases in life expectancy, they did not have larger increases in per-

capita medical expenditure. This may be the case because, while newer diagnostic procedures

and drugs are more expensive than their older counterparts, they may reduce the need for

costly additional medical treatment. The absence of a correlation across states between

medical innovation and expenditure growth is inconsistent with the view that advances in

medical technology have contributed to rising overall U.S. healthcare spending. Increased

health insurance coverage is associated with lower growth in per-capita medical expenditure.

The Impact of New (Orphan) Drug Approvals on Premature Mortality from Rare Diseases in the U.S. and France, 1999-2007

Lichtenberg (2011b) investigated the impact of the introduction of new orphan drugs on

premature mortality from rare diseases using longitudinal, disease-level data obtained from a

number of major databases. The analysis was performed using data from two countries: the

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U.S. (during the period 1999-2006) and France (during the period 2000-2007). For both

countries, I estimated models using two alternative definitions of premature mortality, several

alternative criteria for inclusion in the set of rare diseases, and several values of the potential

lag between new drug approvals and premature mortality reduction.

Both the U.S. and French estimates indicated that, overall, premature mortality from rare

diseases is unrelated to the cumulative number of drugs approved 0-2 years earlier, but is

significantly inversely related to the cumulative number of drugs approved 3-4 years earlier.

This delay is not surprising, since most patients probably don’t have access to a drug until

several years after it has been launched. Earlier access to orphan drugs could result in earlier

reductions in premature mortality from rare diseases.

Although the estimates for the two countries were qualitatively similar, the estimated

magnitudes of the U.S. coefficients are about four times as large as the magnitudes of the

French coefficients. This may be partly due to greater errors in measuring dates of drug

introduction in France. It is also possible that access to new drugs is more restricted in France

than it is in the U.S. Lichtenberg (2009) found that the fraction of cardiovascular drug therapy

days in 2004 that were for “new” drugs — drugs launched after 1995 — was 13% in the U.S.

and 8% in France. My estimates indicate that, in the U.S., potential years of life lost to rare

diseases before age 65 declined at an average annual rate of 3.3%, and that, in the absence of

lagged new drug approvals, potential years of life lost before age 65 (PYLL65) would have

increased at a rate of 0.9%. Since the U.S. population age 0-64 was increasing at the rate of

1.0% per year, this means that PYLL65 per person would have remained approximately

constant. The reduction in the U.S. growth rate of PYLL65 attributable to lagged new drug

approvals was 4.2%.

In France, PYLL65 declined at an average annual rate of 1.8%. The estimates imply that, in the

absence of lagged new drug approvals, it would have declined at a rate of 0.6%. The reduction

in the French growth rate of PYLL65 attributable to lagged new drug approvals was 1.1%.

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Has Pharmaceutical Innovation Reduced Social Security Disability Growth? A number of scholars have argued that medical innovation has played a major role in the long-

term decline in disability. Two studies, Lichtenberg (2005) and Lichtenberg and Virabhak

(2007), investigated whether, in general, the introduction and use of newer prescription drugs

reduces disability. One was based on longitudinal data on a set of diseases, the other was

based on cross-sectional data on individuals. In both cases, disability status was self-reported.

Lichtenberg (2011c) reexamined the question using longitudinal state-level data during the

period 1995-2004. The disability measure I analyzed is the ratio of the number of workers

receiving Social Security Disability Insurance (DI) benefits to the working-age population (the

“DI recipiency rate”). A previous study investigated the behavior of the DI recipiency rate using

longitudinal state-level data during the period 1978-1998, but that study did not include

measures of pharmaceutical use or other potential determinants of health.

I performed an econometric analysis of the effect of pharmaceutical innovation on the DI

recipiency rate, controlling for other potential determinants of health (age, education, and

behavioral risk factors) and other factors (DI program generosity and labor market conditions)

that previous investigators have identified as important influences on DI participation. The

principal contribution of this paper was the incorporation of drug vintage measures in models

of DI recipiency. All of my measures of drug vintage were based on complete data on utilization

of outpatient drugs paid for by state Medicaid agencies, combined with data on the initial FDA

approval dates of the active ingredients of these drugs. Medicaid pays for one in seven U.S

prescriptions.

I estimated models of the DI recipiency rate using alternative measures of drug vintage. The

implications of all of the models were virtually identical. In every case, i.e., regardless of the

precise definition of drug vintage, there was a significant inverse relationship between disability

recipiency and drug vintage. Disability recipiency was also consistently inversely related to the

average wage rate and the fraction of state residents with at least a college education, and

directly related to mean age.

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The existence of a significant inverse relationship between disability recipiency and drug

vintage implies that, if mean drug vintage had not increased — i.e., if people used the same

drugs in 2004 as they had used in 1995 — the DI recipiency rate would have increased more

than it actually did. As shown in Figure 1, from 1995 to 2004, the actual disability rate

increased 30%, from 2.62% to 3.42%. The estimates imply that in the absence of any post-1995

increase in drug vintage, the increase in the disability rate would have been 30% larger: The

disability rate would have increased 39%, from 2.62% to 3.65%. This means that in the absence

of any post-1995 increase in drug vintage, about 418,000 more working-age Americans would

have been DI recipients, and that Social Security benefits paid to disabled workers in 2004

would have been about $4.5 billion higher.

I also explored the reasons for interstate variation in the growth in Medicaid drug vintage.

Some estimates indicated that less financially constrained state governments — those with

higher growth in per-capita tax revenue — may have made newer drugs more available to

3.65%

2.62%

3.42%

2.5%

2.7%

2.9%

3.1%

3.3%

3.5%

3.7%

3.9%

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Figure 1. Predicted disability rate in year t (t = 1996,…,2004), in the absence of any post-1995 increase in drug vintage

predicted disability rate in the absence of any post-1995 increase in drug vintage

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Medicaid patients. But the variable that had the greatest influence on Medicaid drug vintage

was the AIDS incidence rate. States whose AIDS incidence rates fell more slowly than average

had smaller increases in Medicaid drug vintage. This may be because the Medicaid budgets of

states with slowly declining numbers of AIDS cases were under greater stress than the Medicaid

budgets of states with rapidly-declining numbers of AIDS cases. High AIDS incidence may have

increased disability rates among patients with other conditions by causing their access to newer

treatments to be restricted.

The Effect of Pharmaceutical Innovation on the Functional Limitations of Elderly Americans: Evidence from the 2004 National Nursing Home Survey Previous investigators have hypothesized that medical innovation in general, and

pharmaceutical innovation in particular, has made important contributions to the decline in the

functional limitations of older people. Lichtenberg (2012) examined the effect of

pharmaceutical innovation on the functional status of nursing home residents using cross-

sectional, patient-level data from the 2004 National Nursing Home Survey. This was the first

public-use survey of nursing homes that contains detailed information about medication use,

and it contains better data on functional status than previous surveys.

I found that residents using newer medications and a higher proportion of priority-review

medications were more able to perform all five activities of daily living (ADLs), controlling for

age, sex, race, marital status, veteran status, where the resident lived prior to admission,

primary diagnosis at the time of admission, up to 16 diagnoses at the time of the interview,

sources of payment, and facility fixed effects.

The ability of nursing home residents to perform activities of daily living is positively related to

the number of “new” (post-1990) medications they consume, but unrelated to the number of

old medications they consume. As shown in Figure 2, I estimated how much higher the

functional limitations of 2004 nursing home residents would have been had they used old

medications instead of new medications.

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I estimated that the fraction of nursing home residents with all five ADL dependencies (number

of activities for which the resident is not independent) would have been 58% instead of 50%.

During the period 1990-2004, pharmaceutical innovation is estimated to have reduced the

functional limitations of nursing home residents by between 1.2% and 2.1% per year.

Unfortunately, the NNHS does not provide information about some potential determinants of

functional status, such as the resident’s income, wealth, or educational attainment. However,

these other potential determinants are likely to be controlled for, to an important extent, by

factors included in the model, such as race, diagnoses, and sources of payment. The facility

fixed effects are also likely to control for a substantial amount of variation in socioeconomic

status (SES), since SES is likely to play an important role in the “assignment” of residents to

22%

25%

15%

31%

37%

31% 33%

21%

39%

44%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

Transferring Dressing Eating Using the toilet Bathing

Figure 2. Probability of being totally dependent in 2004: actual vs. predicted if only pre-1991 medications used

actual

if only pre-1991 medications used

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facilities. Also, analysis of data by race suggested that there is little or no correlation between

medication attributes and SES.

Have Newer Cardiovascular Drugs Reduced Hospitalization? Evidence from Longitudinal Country-Level Data on 20 OECD Countries, 1995-2003

Lichtenberg (2009) examined the effect of changes in the vintage distribution of cardiovascular

system drugs on hospitalization and mortality due to cardiovascular disease using longitudinal

country-level data on 20 OECD countries during the period 1995-2003.

I found that countries with larger increases in the mean vintage of cardiovascular drug had

smaller increases in the cardiovascular disease hospital discharge rate, controlling for the

quantity of cardiovascular medications consumed per person, the use of other medical

innovations (CT scanners and MRI units), consumption of calories, tobacco, and alcohol, and

demographic variables (population size and age structure, income, and educational

attainment). The estimates also indicated that use of newer cardiovascular drugs has reduced

average length of stay and the age-adjusted cardiovascular mortality rate, but not the number

of potential years of life lost due to cardiovascular disease before age 70 per 100,000

population.

The estimates indicate that if drug vintage had not increased during 1995-2004, hospitalization

and mortality would have been higher in 2004. I estimated that per-capita expenditure on

cardiovascular hospital stays would have been 70% ($89) higher in 2004 had drug vintage not

increased during 1995-2004. Per-capita expenditure on cardiovascular drugs would have been

lower in 2004 had drug vintage not increased during 1995-2004. But our estimate of the

increase in expenditure on cardiovascular hospital stays is about 3.7 times as large as our

estimate of the reduction in per-capita expenditure for cardiovascular drugs that would have

occurred ($24).

Although our data on hospital use and especially on drug utilization were quite complete and

reliable, data on cardiovascular risk factors were less complete. We can think of little reason to

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expect these risk factors to be correlated with drug vintage, conditional on income, education,

and average rate of drug utilization (which we control for). Nevertheless, obtaining better

information on these risk factors would certainly be desirable.

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References Cutler D, Deaton A, Lleras-Muney A (2006). “The Determinants of Mortality,” Journal of Economic Perspectives 20(3, Summer), 97-120.

Dorsey ER (2010), et al, “Financial Anatomy of Biomedical Research, 2003 – 2008,” JAMA, January 13; 303(2): 137–143, http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3118092/

Fuchs, VR (2010), “New Priorities for Future Biomedical Innovations,” N Engl J Med 363:704-706 August 19, http://www.nejm.org/doi/full/10.1056/NEJMp0906597

Kramarow E, Lubitz J, Lentzner H, Gorina Y (2007), “Trends in the Health of Older Americans, 1970–2005,” Health Affairs 16 (5), September, 1417-1425. Lichtenberg FR (2005), “Availability of new drugs and Americans' ability to work,” Journal of Occupational and Environmental Medicine 47 (4), April, 373-380. Lichtenberg FR (2009), “Have newer cardiovascular drugs reduced hospitalization? Evidence from longitudinal country-level data on 20 OECD countries, 1995-2003,” Health Economics 18 (5), 519-534. Lichtenberg FR (2011a), “The quality of medical care, behavioral risk factors, and longevity growth,” International Journal of Health Care Finance and Economics 11(1), March 2011, 1-34.

Lichtenberg FR (2011b), “The impact of new (orphan) drug approvals on premature mortality from rare diseases in the U.S. and France, 1999-2007,” European Journal of Health Economics, 2011 Sep 28. [Epub ahead of print] http://www.ncbi.nlm.nih.gov/pubmed/21953319. Lichtenberg FR (2011c), “Has pharmaceutical innovation reduced Social Security Disability growth?,” International Journal of the Economics of Business 18 (2).

Lichtenberg FR (2011d), “The Impact of Therapeutic Procedure Innovation on Hospital Patient Longevity: Evidence From Western Australia, 2000-2007,” NBER Working Paper No. 17414, http://www.nber.org/papers/w17414

Lichtenberg FR (2012), “The effect of pharmaceutical innovation on the functional limitations of elderly Americans: evidence from the 2004 National Nursing Home Survey,” NBER Working Paper No. 17750, http://www.nber.org/papers/w17750; Advances in Health Economics and Health Services Research 23, 71-99, forthcoming,

Lichtenberg FR, Virabhak S (2007), “Pharmaceutical-embodied technical progress, longevity, and quality of life: drugs as ‘equipment for your health,’” Managerial and Decision Economics 28: 371–392.

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Murphy KM, Topel RH (2006), “The Value of Health and Longevity,” Journal of Political Economy 114 (4), August, 871-904. National Institutes of Health (2012), Impact of NIH Research on Our Health, http://www.nih.gov/about/impact/health.htm Nordhaus WD (2003), “The Health of Nations: The Contribution of Improved Health to Living Standards,” in Measuring the Gains From Medical Research: An Economic Approach, ed. K. M. Murphy and R. H. Topel (Chicago: University of Chicago Press), 9-40. Royal Swedish Academy of Sciences (1987), Press release, http://www.nobelprize.org/nobel_prizes/economics/laureates/1987/press.html Sampat BN, Lichtenberg FR (2011), “What Are the Respective Roles of the Public and Private Sectors in Pharmaceutical Innovation?” Health Affairs 30(2):332-9, Feb. Solow RM (1960), “Investment and Technical Progress,” in K. Arrow, S. Karlin, P. Suppes (Eds.), Mathematical Methods in the Social Sciences, 1959 (University of Chicago Press, Chicago).

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